Explainable Machine Learning with Optimized Tree-Based Models and Statistical Validation for Data-Driven Wear Prediction of Carburized and Non-Carburized Engineering Steels

Accurate prediction of wear behavior in steel materials is essential for enhancing component durability and optimizing manufacturing processes. This study presents a comparative data-driven framework for wear prediction using Decision Tree (DT), Random Forest (RF), and a Physics-Informed Neural Network (PINN). Hyperparameter optimization for the DT and RF models was performed using GridSearchCV with 5-fold cross-validation to obtain optimal model configurations. Model performance was evaluated using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). In addition, sensitivity analysis was conducted by varying train–test split ratios, while residual analysis, confidence interval estimation, and SHAP-based feature importance analysis were employed to assess model reliability and interpretability. The Random Forest model achieved the best predictive performance on the test dataset with RMSE = 0.00495, MAE = 0.00273, and R2 = 0.7244, followed by the Decision Tree model with RMSE = 0.00565, MAE = 0.00275, and R2 = 0.6406. The implemented Physics-Informed Neural Network (PINN), incorporating a load–time physics constraint, exhibited significantly poorer performance, yielding RMSE = 0.15872, MAE = 0.12599, and R2 = −282.62, indicating that the available experimental dataset and simplified physics constraint were insufficient for effective physics-informed learning. SHAP analysis identified surface condition, material type, sliding time, and the load–time interaction as the most influential parameters governing wear behavior. The findings demonstrate that optimized tree-based machine learning models, particularly the Random Forest model, provide accurate, interpretable, and computationally efficient predictions for wear estimation in the investigated steel materials.